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Speech To Image Translation Framework for Teacher-Student Learning 3

Speech To Image Translation Framework for Teacher-Student Le...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Bittal, Vijaylaxmi Bachhav, Anuradha Sanjay Shahade, Makarand Chavan, Pradnya Rajendra Nikam, Bhavesh Anil Pawar, Ajinkya Anil SVKM's Institute of Technology Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350322842

Most of the technical concepts written in textual way and these are communicated orally by teachers to students through limited images. Students not able to understand some of the complex topics in expected way. This will create a gap between subject and student's subject understandability. Representing and conveying through images is a convenient and effective way for Teaching Learning process. Filling this gap is very essential because understanding through visualization will enhance learning process. Though, the combo of speech encoding and computational translation in only one model causes a huge difficulty and many complexities. In order to reduce those efforts, speech to image translation framework plays significant role. With this motivation authors designed a framework for teachers-students learning process. In this framework, authors designed framework to abstract characteristics from audio files and trained model using unsupervised machine learning algorithm. In this firstly, a speech encoder encodes the audio and extract some significant and highlighted features from it and applying algorithm on this feature, Authors perform mapping process on these parameters and features of images which are present in dataset. After this process, GAN comes into play which produce corresponding image as output. In comparison to other methods like speech-text-image, text-image, speech-text models, our model approaches on synthesized dataset and reach to higher expectations of user. © 2023 IEEE.

关键词： Students

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Revolutionizing Traditional Supermarkets: A Comprehensive Solution for Enhancing Efficiency, Security and Customer Satisfaction 2024

Revolutionizing Traditional Supermarkets: A Comprehensive So...

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Tanna, Om H. Vasani, Ankur M. Thakkar, Krishna H. Gotecha, Malav J. Dept. of Information Technology MPSTME NMIMS University India Dept. of Information Technology Shri Bhagubhai Mafatlal Polytechnic India Dept. of Computer Engineering DJ Sanghvi College of Engineering India

ISBN: (纸本)9798400709722

This research proposes an Artificially Intelligent enabled Smart Supermarket System for enhanced efficiency, security and reduces man power as compared to traditional Supermarkets. In order to ensure a perfect omnichannel shopping experience the system uniquely includes an Artificially Intelligent model. Customers can scan items on the go with the app in their smartphones, select product variants as required, add the product to their virtual cart and pay online for the total cart value. With the help of YOLOv5 computer vision algorithm, eliminating long checkout queues and manpower the system proves to be revolutionizing the traditional supermarket procedures. Moreover, the system also incorporates the indoor navigation assistance on customer's app to facilitate easy access and navigation to preferred items. This futuristic solution not only provides valuable insights for business development but also is proven to be more efficient and helpful for customers saving their time and companies resources. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Assessing Water Quality Using Machine Learning

Assessing Water Quality Using Machine Learning

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Nirmala Deve, P. Harithra, S. Keerthana, J. Pavithra, R. Thanyashree, S.J. Sri Sairam Institute of Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394580

For all living things, water is one of the most crucial natural resources. The ability to access clean water is essential for human survival. Beyond drinking, water is also necessary for animal consumption, irrigation, as well as domestic and commercial uses. For the stability and conservation of the environment, it is crucial to monitor the quality of treated wastewater to protect the ecosystem. The issue of poor water quality is getting worse every day due to a number of factors & other dangerous chemicals. Laboratory assessments of water samples to determine their fitness to use is a vital step in the water quality assurance process. However, laboratory assessments require adherence to stringent measures, which might be difficult to comply with. Machine learning (ML) has emerged in recent years as viable and cheaper solutions to replace the lab-based assessments, with a limited availability of sufficient data to train the ML models. Unfortunately, such data is not always available, especially in less developed countries. So that the work attempts to fill this gap by creating sample sized datasets that can be used to train & test the ML models. In simple words the datasets were curated by aggregating data from smaller datasets on related concepts, then processed and labeled to make them useful for supervised ML models. © 2023 IEEE.

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Cross-Database Facial Expression Recognition using CNN with Attention Mechanism 14

Cross-Database Facial Expression Recognition using CNN with ...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Jatin Annappa, B. Rashmi Adyapady, R. National Institute of Technology Dept. of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350335095

Facial expression is one of the most effective and universal ways to express emotions and intentions. It reflects what a person is thinking or experiencing. Thus, the expression recognition is one of the key aspects of understanding non-verbal communication and interpreting emotions in social interactions. Some emotions are very confusing, and separating the features between them becomes difficult because they share the same feature space. For example, the distinction between fear, anger, and disgust is confusing. This work tried to improve the model's class-wise performance to detect each class correctly. A distinct combination of deep-learning models is used to calculate the performance of the model, such as ResNet, XceptionNet, DenseNet, etc. The datasets like Real-world Affective Faces Database (RAF-DB), Japanese Female Facial Expression (JAFFE) & Facial Expression Recognition 2013 Plus (FER+) are used to evaluate the model's performance. The proposed model achieved better results and overcame the previous work's limitations. CDE's performance on RAF-DB and FER+ evaluations was significantly better than the current SOTA methods, with an increase in accuracy of 5.18% and 3.98%, respectively. © 2023 IEEE.

关键词： Face recognition

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Unleashing Customer Insights: Segmentation Through Machine Learning

Unleashing Customer Insights: Segmentation Through Machine L...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Wani, Abbeer Priyanka, M. Prasath, R. KCG College of Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350311204

The Customer Segmentation Analysis project is a machine learning based approach to segmenting customer based on their characteristics and behaviors. The project aims to help businesses to identify their most valuable customers and develop targeted marketing strategy for each segment. The project utilizes various data preprocessing techniques and machine learning algorithms such K-means clustering, Principal Component Analysis (PCA) and hierarchical clustering to segment customers based on the transactional credit card data. The data set used for the analysis is the Online Retail II dataset, which contains customer transaction from an online retailer. The results of the analysis are presented in the form visualization, insights and tabular columns containing the respective silhouette scores obtained against the respective algorithms, these results can be used to develop an effective marketing strategy targeting each customer segment. © 2023 IEEE.

关键词： K-means clustering

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Multimodal biometric authentication using Fully Homomorphic Encryption 2

Multimodal biometric authentication using Fully Homomorphic ...

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2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Vallabhadas, Dilip Kumar Sandhya, Mulagala Sarkar, Soumyadip Chandra, Y Rupesh National Institute of Technology Dept. of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350310719

In this paper multimodal biometric system is developed using two traits iris and fingerprint. The features generated by iris and fingerprint images are fused at the feature level. The generated fused feature vector template cannot be stored directly on the server, if stored directly can lead to various privacy and security concerns. So, these templates are encrypted in such a way that even when applying any operations on the templates, the templates should be in encrypted form. So, the operations need to be performed in the encrypted domain without decrypting it, and the final result, when decrypted should again give back the correct result as if the operations are performed on the original data. Fully Homomorphic encryption (FHE) scheme is designed to satisfy the above conditions. FHE is used to compute the hamming distance between the reference and probe template in an encrypted domain. To improve accuracy rotational invariant technique is used, which solves rotational inconsistency problems. The computational speed is increased by using a batching scheme to reduce the number of operations during homomorphic multiplication. We have conducted our experiment on the IITD and CASIA dataset. The best EER is obtained in CASIA dataset of 0.01% with a computational time of 0.0152 sec per template. © 2023 IEEE.

关键词： Hamming distance

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Distress Detection Using a Hybrid SVM - CNN Classifier 1

Distress Detection Using a Hybrid SVM - CNN Classifier

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Varsha, Modha Aithal, Yukthi R Fathima, Sufia Sen, Snigdha Global Academy of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350339727

Due to the escalating frequency of reported crimes, there has been a surge in research endeavors focused on various approaches to enhance monitoring and surveillance techniques. As opposed to vision-based applications, which are currently the most used framework for monitoring purposes, audio-based systems can be more flexible and relatively less intrusive. While current research studies predominantly utilize images as the primary input for Deep Learning (DL) algorithms, it is worth noting that sound can also serve as a valuable source of input for these models. In this paper, we propose and develop a novel hybrid deep learning model for identifying and detecting people in distress by their screams. The working of our proposed system is built by integrating sound detection module and DL model which will help us to detect if a person is in distress or not. The system uses hybridization concept consisting of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models, with the audio snippet undergoing 3 levels of classification, with the accuracy of each level found to be 93%, 100% and 92% respectively. The audio data is finally classified as an instance of distress or no-distress. © 2023 IEEE.

关键词： Support vector machines

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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation 2

Blockchain Based Secure and Decentralized Smart Licensing of...

引用

2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Haque, Momenul Paul, Subrata Kumer Islam, Kamrul Della, Mursheda Nusrat Paul, Rakhi Rani Fahim, Sultan Bangladesh Army University of Engineering and Technology Dept. of Computer Science and Engineering 6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Bangladesh Army University of Engineering and Technology Dept. of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350358667

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city. © 2023 IEEE.

关键词： Smart contract

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Recognition of Diseases on Horticultural Leaves Using Machine Learning Approach 3

Recognition of Diseases on Horticultural Leaves Using Machin...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Mehta, Pradnya Sapkal, Sarita Lichade, Tanush Kulkarni, Chinmay Patil, Yashowardhan Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350302288

In today's environment, Machine Learning has the potential to make huge strides toward making life better for everyone in the horticulture sector. Disease identification and prediction at the early stage of disease diagnosis will help the farmers get more yields and have a strong economic background. The proposed system uses a neural network algorithm to get insight into using machine learning technology for improving the nation's horticulture in areas like production, crop protection, and day-to-day field monitoring. Our work will also notify us about how various areas of machine learning may be brought together to create a unified result that will aid in the growth of the horticulture industry and, by extension, the nation's overall productivity. Our goal throughout this research was to provide a standardized method for evaluating the development and quality of produce. Because computer vision has so much promise to address issues in rural areas, this research drew on several prior publications for background material. © 2023 IEEE.

关键词： Horticulture

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A weighted ensemble model for phishing website detection using random forest and deep neural network 5

A weighted ensemble model for phishing website detection usi...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Khan, Fabiha Hasan, Mehedi Das, Krishna International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Today, the number of people using the Internet to buy and sell goods and services is rapidly increasing. Website- based applications have become more popular in many business sectors because of their low operating costs and platform independence. User's private information may be stolen as a result of this growth and used maliciously. One method for stealing all of a user's information is phishing, which can send consumers to websites with dangerous content. It is a significant cybersecurity threat that can cause severe financial losses and reputational damage. To avoid falling victim to a phishing attack, it is essential to verify the URL before entering any sensitive information and to use anti-phishing tools and software to detect and prevent phishing attacks. This paper describes an approach to detect phishing websites by developing a weighted ensemble model using random forest (RF) and deep neural networks (DNN). By combining the predictions of these two models, we are achieving higher accuracy than any individual model. With 99% accuracy, the suggested model performs better than the individual models, while the random forest model and the DNN model alone achieve 98% and 73% accuracy, respectively. DNN and RF together can potentially capture different aspects of the data and lead to better predictions. DNNs are highly effective at detecting complex patterns in large datasets, while RF can identify the most important features for classification and handle noisy and missing data. By combining these algorithms, it is possible to leverage their strengths and improve overall accuracy and robustness of detection. © 2023 IEEE.

关键词： Cybersecurity

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